Harmonizing Efficiency: Unleashing Synergy Between Robotic Process Automation and AI for Optimal Business Process Optimization

EasyChair Preprint no. 11941

Abstract

In the contemporary landscape of business operations, organizations are increasingly turning to advanced technologies to streamline and enhance their processes. This abstract explores the transformative potential of integrating Robotic Process Automation (RPA) and Artificial Intelligence (AI) to achieve optimal efficiency in business processes. The synergy between RPA and AI promises to revolutionize traditional workflows by automating repetitive tasks and infusing intelligent decision-making capabilities. Robotic Process Automation, characterized by its ability to mimic human actions in software-based environments, provides a foundation for automating rule-based, routine tasks. Concurrently, Artificial Intelligence, with its cognitive capabilities, empowers systems to learn, adapt, and make informed decisions. The amalgamation of RPA and AI fosters a harmonious ecosystem where machines not only execute tasks at unprecedented speeds but also possess the capacity to analyze data and make nuanced decisions. This abstract delves into real-world applications, showcasing how businesses can leverage this symbiotic relationship to optimize various facets of their operations. From accelerating data processing and reducing errors to enhancing customer experiences through personalized interactions, the combined force of RPA and AI opens avenues for unparalleled business process optimization.

Keyphrases: Artificial Intelligence (AI), Automation, business process, cognitive capabilities, customer experience, data processing, decision making, efficiency, Innovation, Optimization, Robotic Process Automation (RPA), synergy, workflow

@Booklet{EasyChair:11941,
  author = {William Jack and Ajay Sigh},
  title = {Harmonizing Efficiency: Unleashing Synergy Between Robotic Process Automation and AI for Optimal Business Process Optimization},
  howpublished = {EasyChair Preprint no. 11941},

  year = {EasyChair, 2024}}